HCAMP RTL
Data Description
To create a valid data frame consisting of ImPACT scores, PCSS scores, and RTL/RTP information from the SIMS data set, the following steps were taken:
- Injuries labeled as “Head Concussion” were identified and isolated from the SIMS data set.
- Three variables were created to represent the duration of time from onset to RTP step 2 (Return to School Full Time), RTP step 2 to RTP step 7 (Full Return to Activity), and from onset to RTP step 7. These three variables are referred to as:
onset_rtl_total_daysrtl_rtp_total_daysonset_rtp_total_days
- Missing data on the dates of injury onset as well as the achievement of RTP steps 2 and 7 was removed.
- Individuals with a duration of time less than 0 days for the aforementioned three variables were removed.
- This data set then consisted of 827 injuries
- When combined with the ImPACT Post-Injury 1 data set, the removal of missing data left 238 injuries
- A new variable was created (
days_between_injury_test_date) to quantify the duration of time between the onset date (SIMS data set) and the test date (ImPACT data set). Injuries were kept in the data set if the duration of time from onset to test date was between 0-30 days. With injuries outside this range removed, the new data set to be used for analysis consists of 208 injuries.
Data Exploration - SIMS Demographic Information
Gender
Age
League
School
Sport
Sport Level
Data Exploration - SIMS RTP Information
Duration of Time from Onset to RTP Completion
Quick Summary
Gender Summary
Age Summary
League Summary
Visualizations
Age and Gender
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
League and Gender
All Leagues
BIIF
KIF
MIL
Duration of Time from Onset to RTL Completion
Quick Summary
Gender Summary
Age Summary
League Summary
Visualizations
Age and Gender
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
League and Gender
All Leagues
BIIF
KIF
MIL
Duration of Time from RTL Completion to RTP Completion
Quick Summary
Gender Summary
Age Summary
League Summary
Visualizations
Age and Gender
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
League and Gender
All Leagues
BIIF
KIF
MIL
Data Exploration - ImPACT PCSS Scores
Time between Injury Onset and Test Date
To calculate the duration of time from injury onset (onset) and ImPACT test date where PCSS scores were collected (test_date), a variable was created (days_between_injury_test_date) to represent this duration of time. Injuries where testing occurred more than 30 days after the injury were removed from the data set.
Whole Sample
League
PCSS Symptom Category Description
Using Lumba-Brown et al. (2019) and Harmon et al. (2019) as references, the 22 PCSS symptoms were organized into the following six symptom clusters:
- Headache-Migraine Symptoms Cluster (Total Possible Score: 18)
- Headache (PCSS symptom 1)
- Light Sensitivity (PCSS symptom 11)
- Noise Sensitivity (PCSS symptom 12)
- Cognitive Symptoms Cluster (Total Possible Score: 24)
- Difficulty Concentrating (PCSS symptom 20)
- Difficulty Remembering (PCSS symptom 21)
- Feeling “Slow” (PCSS symptom 18)
- Feeling “Foggy” (PCSS symptom 19)
- Anxiety-Mood Symptoms Cluster (Total Possible Score: 30)
- Irritability (PCSS symptom 13)
- Sadness (PCSS symptom 14)
- Nervousness (PCSS symptom 15)
- More Emotional (PCSS symptom 16)
- Numbness (PCSS symptom 17)
- Ocular-Motor Symptoms Cluster (Total Possible Score: 6)
- Visual Problems (PCSS symptom 22)
- Vestibular Symptoms Cluster (Total Possible Score: 24)
- Dizziness (PCSS symptom 5)
- Balance Problems (PCSS symptom 4)
- Nausea (PCSS symptom 2)
- Vomiting (PCSS symptom 3)
- Sleep Symptoms Cluster (Total Possible Score: 30)
- Fatigue (PCSS symptom 6)
- Trouble Falling Asleep (PCSS symptom 7)
- Excessive Sleep (PCSS symptom 8)
- Loss of Sleep (PCSS symptom 9)
- Drowsiness (PCSS symptom 10)
PCSS Total Possible Score: 132
PCSS Score Summaries
Total Score (Total Possible Score: 132)
Summary
Gender Summary
Age Summary
Visualizations
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
Headache-Migraine Symptoms Cluster (Total Possible Score: 18)
Summary
Gender Summary
Age Summary
Visualizations
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
Cognitive Symptoms Cluster (Total Possible Score: 24)
Summary
Gender Summary
Age Summary
Visualizations
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
Anxiety-Mood Symptoms Cluster (Total Possible Score: 30)
Summary
Gender Summary
Age Summary
Visualizations
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
Ocular-Motor Symptoms Cluster (Total Possible Score: 6)
Summary
Gender Summary
Age Summary
Visualizations
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
Vestibular Symptoms Cluster (Total Possible Score: 24)
Summary
Gender Summary
Age Summary
Visualizations
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
Sleep Symptoms Cluster (Total Possible Score: 30)
Summary
Gender Summary
Age Summary
Visualizations
All Ages
Age 14
Age 15
Age 16
Age 17
Age 18
What is the relation between reported symptoms (as measured by the PCSS at the time of ImPACT testing) and the duration of time from injury onset to completion of the full RTP protocol?
Visual Relationships
Total Symptom Score
Headache-Migraine Symptoms Cluster
Cognitive Symptoms Cluster
Anxiety-Mood Symptoms Cluster
Ocular-Motor Symptoms Cluster
Vestibular Symptoms Cluster
Sleep Symptoms Cluster
Correlation
Total Symptom Score
## [1] 0.1571708Headache-Migraine Symptoms Cluster
## [1] 0.1166308Cognitive Symptoms Cluster
## [1] 0.1774048Anxiety-Mood Symptoms Cluster
## [1] 0.130307Ocular-Motor Symptoms Cluster
## [1] 0.2200165Vestibular Symptoms Cluster
## [1] 0.1305177Sleep Symptoms Cluster
## [1] 0.09691213Correlation Table
Normality Check
Extreme outliers exist for all predictor and outcome variables, where predictor variables correspond to the PCSS total symptom and cluster scores, and the outcome variables correspond to the duration of time to complete the RTL and RTP process.
Onset to RTP Completion
PCSS Total Score
Headache-Migraine Symptoms Cluster
Cognitive Symptoms Cluster
Anxiety-Mood Symptoms Cluster
Ocular-Motor Symptoms Cluster
Vestibular Symptoms Cluster
Sleep Symptoms Cluster
Models
Model 1
Linear model of the relationship between total PCSS symptom score and the duration of time to complete RTP protocol
Model strengths:
- Both the intercept and slope have significant p-values, suggesting there is a relationship between total PCSS symptom score and the duration of time to complete the RTP protocol.
- The t-values aren’t very large, but they are far enough away from 0 to suggest the coefficients are not equal to 0 by chance.
Model weakness:
- The model does not account for much variance, as indicated by the R-squared and adjusted R-squared values. This is likely due to the presence of outliers and overall variance in PCSS total symptom scores. In my opinion, this model would be stronger with a larger sample size.
Overall Interpretation:
There does appear to be a relationship between PCSS total symptom score and the duration of time to complete the RTP protocol; however, the model is weakened by the presence of outliers and high level of variance in both the predictor (total PCSS score) and outcome variable (RTP completion time).
##
## Call:
## lm(formula = onset_rtp_total_days ~ total_symptom_score, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.867 -11.907 -7.037 1.702 254.352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.0367 2.6431 8.337 1.08e-14 ***
## total_symptom_score 0.3479 0.1523 2.284 0.0234 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.43 on 206 degrees of freedom
## Multiple R-squared: 0.0247, Adjusted R-squared: 0.01997
## F-statistic: 5.218 on 1 and 206 DF, p-value: 0.02338Model 2
Multiple regression model where gender has been added as a predictor variable.
Model Strength:
- The relationship between PCSS total score and RTP completion time continues to be significant.
Model Weakness:
- The addition of the gender variable did not generate a significant result, suggesting gender is not a strong predictor of RTP completion time.
- The model continues to not account for much variance, evident by the low Adjusted R-squared value.
##
## Call:
## lm(formula = onset_rtp_total_days ~ total_symptom_score + gender,
## data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.627 -11.773 -6.724 2.016 253.048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.4941 4.1929 5.842 2e-08 ***
## total_symptom_score 0.3204 0.1567 2.044 0.0422 *
## genderMale -3.5005 4.6332 -0.756 0.4508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.47 on 205 degrees of freedom
## Multiple R-squared: 0.02741, Adjusted R-squared: 0.01792
## F-statistic: 2.889 on 2 and 205 DF, p-value: 0.05791Model 3
Multiple regression model where age has been added as a predictor variable.
Model Strength:
- The relationship between PCSS total score and RTP completion time continues to be significant.
Model Weakness:
- The addition of the age variable did not generate a significant result, suggesting age is not a strong predictor of RTP completion time.
- The model continues to not account for much variance, evident by the low Adjusted R-squared value.
##
## Call:
## lm(formula = onset_rtp_total_days ~ total_symptom_score + age,
## data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.592 -12.114 -6.381 1.108 250.886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.3843 31.5545 0.488 0.6264
## total_symptom_score 0.3270 0.1553 2.105 0.0365 *
## age14 10.9972 31.8512 0.345 0.7303
## age15 10.6091 31.7837 0.334 0.7389
## age16 1.9603 31.8970 0.061 0.9511
## age17 2.5744 31.9241 0.081 0.9358
## age18 2.6583 32.8377 0.081 0.9356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.53 on 201 degrees of freedom
## Multiple R-squared: 0.04258, Adjusted R-squared: 0.014
## F-statistic: 1.49 on 6 and 201 DF, p-value: 0.1832Models 1-3 Comparison
The total symptom score model has the lowest AIC value (as indicated by the “delta” value), suggested it is the strongest model of the three compared. Lower AIC values indicate stronger model fit.
## predictors delta weight
## 1 total_symptom_score 0.000000 0.59
## 2 total_symptom_score + gender 1.421620 0.29
## 4 gender 3.619591 0.10
## 3 total_symptom_score + age 6.151670 0.03Model 4
Linear model between the headache-migraine symptoms cluster score and duration of time to complete RTP protocol. The headache-migraine coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtp_total_days ~ headache_migraine, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.596 -12.360 -7.662 0.778 258.843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.6616 2.7469 8.250 1.87e-14 ***
## headache_migraine 1.1869 0.7042 1.685 0.0934 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.61 on 206 degrees of freedom
## Multiple R-squared: 0.0136, Adjusted R-squared: 0.008814
## F-statistic: 2.841 on 1 and 206 DF, p-value: 0.09341Model 5
Linear model between the cognitive symptoms cluster score and duration of time to complete RTP protocol. The cognitive coefficient is significant, suggesting there is evidence for a slight relationship between cognitive symptom severity and RTP completion duration. However, it should be stated that this model accounts for very little variance as indicated by the R-squared and Adjusted R-squared values.
##
## Call:
## lm(formula = onset_rtp_total_days ~ cognitive, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.789 -11.084 -7.051 1.949 255.080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.0514 2.5387 8.686 1.16e-15 ***
## cognitive 1.5410 0.5956 2.587 0.0104 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.33 on 206 degrees of freedom
## Multiple R-squared: 0.03147, Adjusted R-squared: 0.02677
## F-statistic: 6.694 on 1 and 206 DF, p-value: 0.01036Model 6
Linear model between the anxiety-mood symptoms cluster score and duration of time to complete RTP protocol. The anxiety-mood coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtp_total_days ~ anxiety_mood, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.921 -11.576 -6.694 1.541 257.250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.4587 2.4300 9.654 <2e-16 ***
## anxiety_mood 1.4701 0.7794 1.886 0.0607 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.56 on 206 degrees of freedom
## Multiple R-squared: 0.01698, Adjusted R-squared: 0.01221
## F-statistic: 3.558 on 1 and 206 DF, p-value: 0.06066Model 7
Linear model between the ocular-motor symptoms cluster score and duration of time to complete RTP protocol. The ocular-motor coefficient is significant, suggesting there is evidence for a slight relationship between ocular-motor symptom severity and RTP completion duration. However, it should be stated that this model accounts for very little variance as indicated by the R-squared and Adjusted R-squared values. I also wonder how sensitive this model is as the ocular-motor cluster corresponds to only one symptom, while the other clusters correspond to 3-5 symptoms.
##
## Call:
## lm(formula = onset_rtp_total_days ~ ocular_motor, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.860 -10.433 -6.183 1.067 251.258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.183 2.264 10.239 < 2e-16 ***
## ocular_motor 8.280 2.558 3.237 0.00141 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.05 on 206 degrees of freedom
## Multiple R-squared: 0.04841, Adjusted R-squared: 0.04379
## F-statistic: 10.48 on 1 and 206 DF, p-value: 0.001407Model 8
Linear model between the vestibular symptoms cluster score and duration of time to complete RTP protocol. The vestibular coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtp_total_days ~ vestibular, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.715 -11.490 -7.384 0.767 253.588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.2325 2.4835 9.355 <2e-16 ***
## vestibular 1.5755 0.8339 1.889 0.0602 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.56 on 206 degrees of freedom
## Multiple R-squared: 0.01703, Adjusted R-squared: 0.01226
## F-statistic: 3.57 on 1 and 206 DF, p-value: 0.06024Model 9
Linear model between the sleep symptoms cluster score and duration of time to complete RTP protocol. The sleep coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtp_total_days ~ sleep, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.587 -12.017 -6.901 1.459 261.436
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 23.5408 2.5875 9.098 <2e-16 ***
## sleep 0.8604 0.6157 1.398 0.164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.68 on 206 degrees of freedom
## Multiple R-squared: 0.009392, Adjusted R-squared: 0.004583
## F-statistic: 1.953 on 1 and 206 DF, p-value: 0.1638Cognitive and Ocular-Motor Model
The relationship between the cognitive symptom cluster and ocular-motor symptom cluster was analyzed with a multiple regression model. The results of the model reduced the cognitive coefficient to non-significance, while the ocular-motor coefficient remained slightly significant. As with the previous models, the amount of variance accounted by the model continues to be low.
##
## Call:
## lm(formula = onset_rtp_total_days ~ cognitive + ocular_motor,
## data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.445 -10.368 -6.368 1.632 250.094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22.3677 2.5240 8.862 3.81e-16 ***
## cognitive 0.5588 0.7610 0.734 0.4636
## ocular_motor 6.7544 3.2970 2.049 0.0418 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 31.08 on 205 degrees of freedom
## Multiple R-squared: 0.0509, Adjusted R-squared: 0.04164
## F-statistic: 5.497 on 2 and 205 DF, p-value: 0.004724Cognitive and Ocular-Motor Model Comparison
The ocular motor model is the strongest model, but there could possibly be a case to include cognition in a multiple regression model. Overall, these two symptom clusters appear to be the strongest predictors of RTP duration, but all models are limited due the low R and Adjusted R values. This is likely due to a smaller sample size, variance in predictor and outcome variables, and the presence of outliers.
## predictors delta weight
## 2 ocular_motor 0.000000 0.61
## 3 cognitive + ocular_motor 1.453572 0.29
## 1 cognitive 3.669073 0.10What is the relation between reported symptoms (as measured by the PCSS at the time of ImPACT testing) and the duration of time from injury onset to completion of the RTL (RTP protcol step 2)?
Visual Relationships
Total Symptom Score
Headache-Migraine Symptoms Cluster
Cognitive Symptoms Cluster
Anxiety-Mood Symptoms Cluster
Ocular-Motor Symptoms Cluster
Vestibular Symptoms Cluster
Sleep Symptoms Cluster
Correlation
Total Symptom Score
## [1] 0.07304613Headache-Migraine Symptoms Cluster
## [1] 0.02707833Cognitive Symptoms Cluster
## [1] 0.109808Anxiety-Mood Symptoms Cluster
## [1] 0.08910868Ocular-Motor Symptoms Cluster
## [1] -0.02879951Vestibular Symptoms Cluster
## [1] -0.007090639Sleep Symptoms Cluster
## [1] 0.09902616Correlation Table
Normality Check
Onset to RTL Completion
Extreme outliers present.
Models
Model 1
Linear model of the relationship between total PCSS symptom score and the duration of time to complete RTL protocol. The coefficient of PCSS total symptom score is not significant, suggesting there is not a relationship between total PCSS symptom severity and duration of time to RTL.
##
## Call:
## lm(formula = onset_rtl_total_days ~ total_symptom_score, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6334 -3.0252 -1.5735 0.7311 22.9504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.02521 0.40228 10.006 <2e-16 ***
## total_symptom_score 0.02437 0.02318 1.051 0.294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.784 on 206 degrees of freedom
## Multiple R-squared: 0.005336, Adjusted R-squared: 0.0005073
## F-statistic: 1.105 on 1 and 206 DF, p-value: 0.2944Model 2
Linear regression model between gender and duration of time to complete RTL. The gender coefficient is not significant, suggesting the model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ gender, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9744 -2.8385 -1.8385 0.1615 22.0256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9744 0.5395 9.220 <2e-16 ***
## genderMale -1.1359 0.6825 -1.664 0.0976 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.765 on 206 degrees of freedom
## Multiple R-squared: 0.01327, Adjusted R-squared: 0.008479
## F-statistic: 2.77 on 1 and 206 DF, p-value: 0.09756Model 3
Linear regression model between age and duration of time to complete RTL. The age coefficient is not significant, suggesting the model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ age, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.064 -3.061 -1.525 0.814 21.936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.000 4.794 0.626 0.532
## age14 1.061 4.843 0.219 0.827
## age15 2.063 4.832 0.427 0.670
## age16 0.186 4.850 0.038 0.969
## age17 1.525 4.854 0.314 0.754
## age18 1.000 4.990 0.200 0.841
##
## Residual standard error: 4.794 on 202 degrees of freedom
## Multiple R-squared: 0.02055, Adjusted R-squared: -0.003696
## F-statistic: 0.8476 on 5 and 202 DF, p-value: 0.5175Model 4
Multiple regression model with PCSS total symptom score and gender as predictor variables. Neither coefficient is significant, suggesting the model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ total_symptom_score + gender,
## data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0543 -2.7462 -1.7201 0.2676 22.2396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.74402 0.63575 7.462 2.38e-12 ***
## total_symptom_score 0.01633 0.02376 0.687 0.493
## genderMale -1.02391 0.70251 -1.458 0.147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.771 on 205 degrees of freedom
## Multiple R-squared: 0.01554, Adjusted R-squared: 0.005933
## F-statistic: 1.618 on 2 and 205 DF, p-value: 0.2009Model 5
Multiple regression model with PCSS total symptom score and age as predictor variables. Neither coefficient is significant, suggesting the model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ total_symptom_score + age,
## data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3875 -2.8740 -1.7802 0.6497 22.1288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.85248 4.80269 0.594 0.553
## total_symptom_score 0.01844 0.02364 0.780 0.436
## age14 1.02999 4.84785 0.212 0.832
## age15 2.00027 4.83758 0.413 0.680
## age16 0.23365 4.85483 0.048 0.962
## age17 1.47844 4.85895 0.304 0.761
## age18 0.86324 4.99799 0.173 0.863
##
## Residual standard error: 4.799 on 201 degrees of freedom
## Multiple R-squared: 0.0235, Adjusted R-squared: -0.005645
## F-statistic: 0.8063 on 6 and 201 DF, p-value: 0.5661Models 1-5 Comparison
The gender model has the lowest AIC value (as indicated by the “delta” value), suggested it is the strongest model of the five compared. Lower AIC values indicate stronger model fit. However, none of the individal models are significant, likely due to the large amount of variance in the outcome variable (RTL duration).
## predictors delta weight
## 2 gender 0.000000 0.51
## 4 total_symptom_score + gender 1.521277 0.24
## 1 total_symptom_score 1.665586 0.22
## 3 age 6.459847 0.02
## 5 total_symptom_score + age 7.831214 0.01Model 6
Linear model between the headache-migraine symptoms cluster score and duration of time to complete RTL protocol. The headache-migraine coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ headache_migraine, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4990 -3.1668 -1.3537 0.6256 22.7917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.16679 0.41667 10.000 <2e-16 ***
## headache_migraine 0.04153 0.10682 0.389 0.698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.795 on 206 degrees of freedom
## Multiple R-squared: 0.0007332, Adjusted R-squared: -0.004118
## F-statistic: 0.1512 on 1 and 206 DF, p-value: 0.6978Model 7
Linear model between the cognitive symptoms cluster score and duration of time to complete RTL protocol. The cognitive coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ cognitive, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8160 -2.9472 -1.9472 0.4012 23.0528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.94720 0.38642 10.215 <2e-16 ***
## cognitive 0.14375 0.09066 1.586 0.114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.768 on 206 degrees of freedom
## Multiple R-squared: 0.01206, Adjusted R-squared: 0.007262
## F-statistic: 2.514 on 1 and 206 DF, p-value: 0.1144Model 8
Linear model between the anxiety-mood symptoms cluster score and duration of time to complete RTL protocol. The anxiety-mood coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ anxiety_mood, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7862 -3.0590 -1.3620 0.6607 22.9410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0590 0.3679 11.033 <2e-16 ***
## anxiety_mood 0.1515 0.1180 1.284 0.201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.778 on 206 degrees of freedom
## Multiple R-squared: 0.00794, Adjusted R-squared: 0.003125
## F-statistic: 1.649 on 1 and 206 DF, p-value: 0.2006Model 9
Linear model between the ocular-motor symptoms cluster score and duration of time to complete RTL protocol. The ocular-motor coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ ocular_motor, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3092 -2.4517 -1.3092 0.6908 22.6908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3092 0.3497 12.324 <2e-16 ***
## ocular_motor -0.1633 0.3950 -0.414 0.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.795 on 206 degrees of freedom
## Multiple R-squared: 0.0008294, Adjusted R-squared: -0.004021
## F-statistic: 0.171 on 1 and 206 DF, p-value: 0.6797Model 10
Linear model between the vestibular symptoms cluster score and duration of time to complete RTL protocol. The vestibular coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ vestibular, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2826 -3.1149 -1.2826 0.7174 22.7174
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2826 0.3775 11.345 <2e-16 ***
## vestibular -0.0129 0.1268 -0.102 0.919
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.797 on 206 degrees of freedom
## Multiple R-squared: 5.028e-05, Adjusted R-squared: -0.004804
## F-statistic: 0.01036 on 1 and 206 DF, p-value: 0.919Model 11
Linear model between the sleep symptoms cluster score and duration of time to complete RTL protocol. The sleep coefficient is not significant, suggesting this model is not a good fit.
##
## Call:
## lm(formula = onset_rtl_total_days ~ sleep, data = model_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6926 -2.9701 -1.7651 0.7199 23.0299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.97012 0.38988 10.183 <2e-16 ***
## sleep 0.13250 0.09277 1.428 0.155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.773 on 206 degrees of freedom
## Multiple R-squared: 0.009806, Adjusted R-squared: 0.004999
## F-statistic: 2.04 on 1 and 206 DF, p-value: 0.1547Symptom Cluster Model Comparison
The cognitive symptom cluster appears to be the strongest model and individual symptom cluster predictor of RTL duration; however, no individual model for symptom cluster or total PCSS score is significant to justify a relationship between PCSS symptom severity at the time of ImPACT testing and RTL duration. The lack of a relationship is likely due to the presence of outliers and variance in RTL duration across the sample size.
## predictors delta weight
## 2 cognitive 0.0000000 0.30
## 6 sleep 0.4735140 0.24
## 3 anxiety_mood 0.8650797 0.19
## 1 headache_migraine 2.3706970 0.09
## 4 ocular_motor 2.3506768 0.09
## 5 vestibular 2.5128081 0.09